CN103440648B - The method for automatic measurement of corps canopy regularity and device - Google Patents
The method for automatic measurement of corps canopy regularity and device Download PDFInfo
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Abstract
The invention provides a kind of method for automatic measurement and device of corps canopy regularity, relate to technical field of agricultural information.The method comprises the following steps: S1, acquisition corps canopy image; S2, binocular image reconstruction technique is utilized to calculate the three dimensional point cloud of described corps canopy image; S3, from described three dimensional point cloud, isolate the row of crops to cloud data; S4, according to described row to cloud data, calculate the basic data of corps canopy regularity; S5, calculate corps canopy regularity index according to described basic data.The invention solves continuously, obtain automatically the problem of corps canopy regularity index, can automatically, continuously, harmless corps canopy regularity is measured, the method is compared to previous methods, need less human input, automaticity is high, measurement result can combined reaction corps canopy regularity, and measurement result is by the impact of personal error and survey crew experience.
Description
Technical field
The present invention relates to technical field of agricultural information, be specifically related to a kind of method for automatic measurement and device of corps canopy regularity.
Background technology
Maize canopy regularity comprises two aspects, the degree of uniformity that first plant distributes in field, and second is the difference size of proterties between individual plants.Investigation maize canopy regularity has certain reality and theory significance.From real viewpoint, plant is when field skewness, and a part of plant can obtain sufficient illumination, nutrient etc., and another part plant then lacks these conditions, thus affects giving full play to of plant production potential.In agricultural production one to ensure as much as possible from various aspects such as seed selection, whole, sowing, field management the distribution of field crops and growth even, regularity will reach what kind of process is just unlikely to be affected output and save labour and cost actually, just becomes a realistic problem.In theory, plant itself has and adapts to external condition the ability regulating it to grow according to external condition, it tiller, the regulating measure that branch's habit, phototropism, chmotropism etc. are all it.The effect of this adjustment has much, and rule how, how to be used, and is very worth research.
The method investigating milpa canopy distribution regularity for a long time mainly relies on artificial naked eyes to judge, namely by artificial visual inspection canopy distribution, provides the evaluation of canopy regularity according to the empirical judgement of people.Or manual measurement one, several index that can reflect milpa canopy regularity, as the regularity of plant height in canopy, the plant height in manual measurement certain limit, the inverse getting the coefficient of variation of plant height value within the scope of this weighs canopy regularity.
The manual measurement method of maize canopy regularity needs at substantial manpower, not easily carries out on a large scale, and manual measurement mode depends on the skilled operation degree of survey crew, by force empirical, is difficult to remove the personal error in measurement result.
Summary of the invention
(1) technical matters solved
For the deficiencies in the prior art, the invention provides a kind of method for automatic measurement and device of corps canopy regularity, solve the problem obtaining corps canopy regularity index automatically.
(2) technical scheme
For realizing above object, the present invention is achieved by the following technical programs:
A method for automatic measurement for corps canopy regularity, comprises the following steps:
S1, acquisition corps canopy image;
S2, binocular image reconstruction technique is utilized to calculate the three dimensional point cloud of described corps canopy image;
S3, from described three dimensional point cloud, isolate the row of crops to cloud data;
S4, according to described row to cloud data, calculate the basic data of corps canopy regularity;
S5, calculate corps canopy regularity index according to described basic data.
Preferably, step is comprised in step S3:
S31, gray-scale value according to the pixel of described corps canopy image, be separated trip to pixel from the pixel of described corps canopy image;
S32, travel through described row to pixel, with predetermined threshold to image binaryzation, obtain bianry image;
S33, filtering and noise reduction is carried out to described bianry image, removes isolated island pixel, in the middle of filling hole operation after; The three-dimensional point cloud corresponding with white pixel in bianry image is that crop row is to cloud data.
Preferably, step is comprised in step S4:
S41, described row is used least square method to pixel, simulate a row to straight line;
S42, described row is mapped in described three dimensional point cloud to straight line, by described row to axle centered by straight line, with the canopy cloud data in the right cylinder of pre-set radius as the basic data calculating corps canopy regularity.
Preferably, comprise in step S5:
S51, calculate described basic data comprise the minimum outsourcing rectangular parallelepiped of space point set;
S52, described minimum outsourcing rectangular parallelepiped is divided into 2
nindividual decile rectangular parallelepiped, travels through each decile rectangular parallelepiped, carries out plane fitting to the cloud data be included in each decile rectangular parallelepiped, obtains plane set;
The angle α that S53, upper and lower, that all around the is adjacent plane calculating plane in described plane set and this plane are formed, and the centre distance d of described plane and adjacent plane
p, and calculate the distance function f of described plane and adjacent plane;
S54, described distance function f to be judged, if f is less than threshold value M, then merge described plane and adjacent plane;
The operation of S55, repetition step S53 ~ S54, when any plane distance function f in described plane set is more than or equal to threshold value M, merges cut-off; And by occupy in described plane set rectangular parallelepiped number be less than threshold value N plane remove.
Preferably, the expression formula calculating distance function f in step S53 is:
In formula, e
1=0.7, e
2=0.3, len be described basic data comprise the minimum long limit of outsourcing rectangular parallelepiped of space point set
Preferably, the method merging described plane and adjacent plane in step S54 is: the planar band new with the point-cloud fitting in two adjacent decile rectangular parallelepipeds replaces original plane.
Preferably, the expression formula calculating corps canopy regularity index in step S5 is:
In formula, p=t/sump, represents the probability that each rectangular parallelepiped midplane occurs, wherein t=0 or 1, sump is plane number sum.
Preferably, the threshold value M in step S5 is 0.45, threshold value N is 4.
Present invention also offers a kind of self-operated measuring unit of corps canopy regularity, comprise with lower module:
Obtain canopy image module, obtain corps canopy image by binocular solid camera;
Calculate three dimensional point cloud module, utilize binocular image reconstruction technique to calculate the three dimensional point cloud of described corps canopy image;
Separate rows, to cloud data module, isolates the row of crops to cloud data from described three dimensional point cloud;
Calculate basic data module, according to described row to cloud data, calculate the basic data of corps canopy regularity;
Calculate canopy regularity Index module, calculate corps canopy regularity index according to described basic data.
Present invention also offers a kind of self-operated measuring unit of corps canopy regularity, comprise with lower module:
Obtain canopy image module, obtain corps canopy image by binocular solid camera;
Calculate three dimensional point cloud module, utilize binocular image reconstruction technique to calculate the three dimensional point cloud of described corps canopy image;
Separate rows, to cloud data module, isolates the row of crops to cloud data from described three dimensional point cloud;
Calculate basic data module, according to described row to cloud data, calculate the basic data of corps canopy regularity;
Calculate canopy regularity Index module, calculate corps canopy regularity index according to described basic data.
(3) beneficial effect
The present invention is by providing a kind of method for automatic measurement and device of corps canopy regularity, by obtaining corps canopy image, obtain the three dimensional point cloud of crops, and then the row obtaining crops is to cloud data, and then obtain calculating corps canopy regularity index, judge corps canopy regularity by this index.The present invention can automatically, continuously, harmless corps canopy regularity is measured.
The present invention is compared to previous methods, and need less human input, automaticity is high, and measurement result can combined reaction corps canopy regularity, and measurement result is by the impact of personal error and survey crew experience.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the schematic flow sheet of the method for automatic measurement of a kind of corps canopy regularity of the embodiment of the present invention;
Fig. 2 is the structural representation of the self-operated measuring unit of a kind of corps canopy regularity of the embodiment of the present invention.
Embodiment
For making the object of the embodiment of the present invention, technical scheme and advantage clearly, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Embodiment 1:
As shown in Figure 1, embodiments provide a kind of method for automatic measurement of corps canopy regularity, the method comprises the following steps:
S1, acquisition corps canopy image;
S2, binocular image reconstruction technique is utilized to calculate the three dimensional point cloud of described corps canopy image;
S3, from described three dimensional point cloud, isolate the row of crops to cloud data;
S4, according to described row to cloud data, calculate the basic data of corps canopy regularity;
S5, calculate corps canopy regularity index according to described basic data.
The embodiment of the present invention, by obtaining corps canopy image, obtains the three dimensional point cloud of crops, and then the row obtaining crops is to cloud data, and then obtains calculating corps canopy regularity index, judges corps canopy regularity by this index.The embodiment of the present invention can automatically, continuously, harmless corps canopy regularity is measured;
Below the embodiment of the present invention is described in detail:
A method for automatic measurement for maize canopy regularity, the method comprises the following steps:
S1, acquisition corps canopy image;
The present embodiment adopts binocular solid camera, model mvcsam1000-30st (the micro-view picture in Beijing), camera distance canopy top about 100cm, and vertically ground is placed, and moves camera 10m along corn line direction, obtains piece image every 50cm.
Shooting time should select sunny in the afternoon carrying out, to remove sunlight and wind to the interference obtaining picture quality.
S2, binocular image reconstruction technique is utilized to calculate the three dimensional point cloud of described maize canopy image;
According to the binocular camera inside and outside parameter of having demarcated in advance, use binocular image reconstruction technique calculates maize canopy three dimensional point cloud, and according to the projection relation of video camera by the pixel one_to_one corresponding on three dimensional point cloud and image.
The projection matrix of binocular camera is Pi, is made up of the binocular camera inside and outside parameter of having demarcated.
If 1 M (X, Y, Z) on canopy plant, m
1(x
1, y
1), m
2(x
2, y
2) be respectively the image coordinate of M point subpoint on the width image of left and right two, then
Wherein: (x
1, y
1, 1), (x
2, y
2, 1) and be respectively m1, m2 homogeneous coordinates in respective image; (X, Y, Z, 1) puts the homogeneous coordinates under world coordinates for M (X, Y, Z); Wi is non-zero parameter; a
k mn(k=1,2; M=1,2,3; N=1,2,3,4) be projection matrix P
ielement in (i=1,2), represents internal reference matrix (focal length, distortion) and the outer ginseng matrix (translation, rotation) of video camera.According to the coordinate m of measured point M in video camera image planes
1(x
1, y
1), m
2(x
2, y
2) and formula (2), just can obtain the world coordinates (X, Y, Z) of unknown point M, calculation expression is as follows:
S3, from described three dimensional point cloud, isolate the row of corn to cloud data;
Preferably, the row isolating corn to cloud data method is:
S31, gray-scale value according to the pixel of described maize canopy image, be separated trip to pixel from the pixel of described maize canopy image;
S32, travel through described row to pixel, with gray-scale value 185 be threshold value to image binaryzation, obtain bianry image;
S33, filtering and noise reduction is carried out to described bianry image, removes isolated island pixel, in the middle of filling hole operation after; The three-dimensional point cloud corresponding with white pixel in bianry image is that corn is capable of cloud data.
S4, according to described row to cloud data, calculate the basic data of maize canopy regularity;
Preferably, the method calculating the basic data of maize canopy regularity is:
S41, described row is used least square method to pixel, simulate a row to straight line.
Suppose there is N number of data point (x
iy
i, i=1,2,3 ... n), one can be able to be defined as by the model of relation between representative function independent variable and dependent variable:
y(x)＝y(x；a
1…a
M)
A
1... a
mit is the coefficient of model.Ask calculation model coefficient can obtain solution to model and analyse form, general by least square fitting model, calculate the minimum value of following formula, obtain a
1... a
mmaximal possibility estimation.
S42, described row is mapped in described three dimensional point cloud to straight line, by described row to axle centered by straight line, with pre-set radius be 15cm right cylinder in canopy cloud data as the basic data calculating corps canopy regularity.
S5, calculate corps canopy regularity index according to described basic data.
Preferably, calculate corps canopy regularity and refer to that calibration method is:
S51, calculate described basic data comprise the minimum outsourcing rectangular parallelepiped of space point set;
S52, described minimum outsourcing rectangular parallelepiped is divided into 64 decile rectangular parallelepipeds, travels through each decile rectangular parallelepiped, plane fitting is carried out to the cloud data be included in each decile rectangular parallelepiped, obtain plane set;
The angle α that S53, upper and lower, that all around the is adjacent any plane calculating plane in described plane set and this plane are formed, and the centre distance d of described plane and arbitrary adjacent plane
p, and calculate the distance function f of described plane;
Preferably, the expression formula calculating distance function f in step S53 is:
In formula, e
1=0.7, e
2=0.3, len be described basic data comprise the minimum long limit of outsourcing rectangular parallelepiped of space point set
S54, described distance function f to be judged, if f is less than threshold value 0.45, then merge described plane and adjacent plane;
Preferably, the method merging described plane and adjacent plane in step S54 is: the planar band new with the point-cloud fitting in two adjacent decile rectangular parallelepipeds replaces original plane.
The operation of S55, repetition step S53 ~ S54, when any plane distance function f in described plane set is more than or equal to threshold value 0.45, merges cut-off; And by occupy in described plane set rectangular parallelepiped number be less than threshold value 4 plane remove.
Preferably, the expression formula calculating corps canopy regularity index in step S5 is:
In formula, p=t/sump, represents the probability that each rectangular parallelepiped midplane occurs, wherein t=0 or 1, sump is plane number sum.
Embodiment 2:
The embodiment of the present invention additionally provides a kind of self-operated measuring unit of maize canopy regularity, comprises with lower module:
Obtain canopy image module, obtain maize canopy image by binocular solid camera;
Calculate three dimensional point cloud module, utilize binocular image reconstruction technique to calculate the three dimensional point cloud of described maize canopy image;
Separate rows, to cloud data module, isolates the row of corn to cloud data from described three dimensional point cloud;
Calculate basic data module, according to described row to cloud data, calculate the basic data of maize canopy regularity;
Calculate canopy regularity Index module, calculate maize canopy regularity index according to described basic data.
To sum up, the method and apparatus that the embodiment of the present invention provides is compared to previous methods and device, and need less human input, automaticity is high, measurement result can combined reaction maize canopy regularity, and measurement result is by the impact of personal error and survey crew experience.
It should be noted that, in this article, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, article or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, article or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment comprising described key element and also there is other identical element.
It should be noted that, the method for automatic measurement of the canopy regularity that the embodiment of the present invention provides and device, be not only applicable to and corn, be also applicable to other as the measurement needs of the crops such as soybean, Chinese sorghum.
Above embodiment only in order to technical scheme of the present invention to be described, is not intended to limit; Although with reference to previous embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein portion of techniques feature; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the spirit and scope of various embodiments of the present invention technical scheme.
Claims (5)
1. a method for automatic measurement for corps canopy regularity, is characterized in that, comprises the following steps:
S1, acquisition corps canopy image;
S2, binocular image reconstruction technique is utilized to calculate the three dimensional point cloud of described corps canopy image;
S3, from described three dimensional point cloud, isolate the row of crops to cloud data;
S4, according to described row to cloud data, calculate the basic data of corps canopy regularity;
S5, calculate corps canopy regularity index according to described basic data;
Step is comprised in step S3:
S31, gray-scale value according to the pixel of described corps canopy image, be separated trip to pixel from the pixel of described corps canopy image;
S32, travel through described row to pixel, with predetermined threshold to image binaryzation, obtain bianry image;
S33, filtering and noise reduction is carried out to described bianry image, removes isolated island pixel, in the middle of filling hole operation after; The three-dimensional point cloud corresponding with white pixel in bianry image is that crop row is to cloud data;
Step is comprised in step S4:
S41, described row is used least square method to pixel, simulate a row to straight line;
S42, described row is mapped in described three dimensional point cloud to straight line, by described row to axle centered by straight line, with the canopy cloud data in the right cylinder of pre-set radius as the basic data calculating corps canopy regularity;
Comprise in step S5:
S51, calculate described basic data comprise the minimum outsourcing rectangular parallelepiped of space point set;
S52, described minimum outsourcing rectangular parallelepiped is divided into 2
nindividual decile rectangular parallelepiped, travels through each decile rectangular parallelepiped, carries out plane fitting to the cloud data be included in each decile rectangular parallelepiped, obtains plane set;
The angle α that S53, upper and lower, that all around the is adjacent plane calculating plane in described plane set and this plane are formed, and the centre distance d of described plane and adjacent plane
p, and calculate the distance function f of described plane and adjacent plane;
S54, described distance function f to be judged, if f is less than threshold value M, then merge described plane and adjacent plane;
The operation of S55, repetition step S53 ~ S54, when any plane distance function f in described plane set is more than or equal to threshold value M, merges cut-off; And by occupy in described plane set rectangular parallelepiped number be less than threshold value N plane remove;
The expression formula calculating corps canopy regularity index in step S5 is:
In formula, p=t/sump, represents the probability that each rectangular parallelepiped midplane occurs, wherein t=0 or 1, sump is plane number sum.
2. method for automatic measurement as claimed in claim 1, it is characterized in that, the expression formula calculating distance function f in step S53 is:
In formula, e
1=0.7, e
2=0.3, len be described basic data comprise the minimum long limit of outsourcing rectangular parallelepiped of space point set
3. method for automatic measurement as claimed in claim 1, it is characterized in that, the method merging described plane and adjacent plane in step S54 is: the planar band new with the point-cloud fitting in two adjacent decile rectangular parallelepipeds replaces original plane.
4. method for automatic measurement as claimed in claim 1, it is characterized in that, the threshold value M in step S5 is 0.45, threshold value N is 4.
5. a self-operated measuring unit for corps canopy regularity, is characterized in that, comprises with lower module:
Obtain canopy image module, obtain corps canopy image by binocular solid camera;
Calculate three dimensional point cloud module, utilize binocular image reconstruction technique to calculate the three dimensional point cloud of described corps canopy image;
Separate rows, to cloud data module, isolates the row of crops to cloud data from described three dimensional point cloud;
Calculate basic data module, according to described row to cloud data, calculate the basic data of corps canopy regularity;
Calculate canopy regularity Index module, calculate corps canopy regularity index according to described basic data;
Wherein, the described three dimensional point cloud utilizing binocular image reconstruction technique to calculate described corps canopy image comprises:
According to the gray-scale value of the pixel of described corps canopy image, from the pixel of described corps canopy image, be separated trip to pixel;
Travel through described row to pixel, with predetermined threshold to image binaryzation, obtain bianry image;
Filtering and noise reduction is carried out to described bianry image, removes isolated island pixel, after filling the operation of middle hole; The three-dimensional point cloud corresponding with white pixel in bianry image is that crop row is to cloud data;
Described according to described row to cloud data, the basic data calculating corps canopy regularity comprises:
Described row is used least square method to pixel, simulates a row to straight line;
Described row is mapped in described three dimensional point cloud to straight line, by described row to axle centered by straight line, with the canopy cloud data in the right cylinder of pre-set radius as the basic data calculating corps canopy regularity;
Describedly calculate corps canopy regularity index according to described basic data and comprise:
Calculate described basic data comprise the minimum outsourcing rectangular parallelepiped of space point set;
Described minimum outsourcing rectangular parallelepiped is divided into 2
nindividual decile rectangular parallelepiped, travels through each decile rectangular parallelepiped, carries out plane fitting to the cloud data be included in each decile rectangular parallelepiped, obtains plane set;
The angle α that upper and lower, that all around the is adjacent plane calculating plane in described plane set and this plane is formed, and the centre distance d of described plane and adjacent plane
p, and calculate the distance function f of described plane and adjacent plane;
Described distance function f is judged, if f is less than threshold value M, then merges described plane and adjacent plane;
The angle α that upper and lower, that all around the is adjacent plane of the plane in plane set described in double counting and this plane is formed, and the centre distance d of described plane and adjacent plane
p, and calculate the distance function f of described plane and adjacent plane;
Described distance function f is judged, if f is less than threshold value M, then merges described plane and adjacent plane;
When any plane distance function f in described plane set is more than or equal to threshold value M, merge cut-off; And by occupy in described plane set rectangular parallelepiped number be less than threshold value N plane remove;
The expression formula calculating corps canopy regularity index is:
In formula, p=t/sump, represents the probability that each rectangular parallelepiped midplane occurs, wherein t=0 or 1, sump is plane number sum.
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| CN103914786B (en) * | 2014-03-10 | 2017-06-30 | 北京农业信息技术研究中心 | A kind of broadacre agriculture produces context aware computational methods and device |
| CN106950573B (en) * | 2017-02-23 | 2019-09-10 | 北京农业信息技术研究中心 | A kind of corn waterlogged disaster appraisal procedure and system based on unmanned plane laser radar |
| CN109816680B (en) * | 2018-12-19 | 2020-10-27 | 黑龙江八一农垦大学 | High-throughput calculation method for crop plant height |
| CN110288594B (en) * | 2019-07-02 | 2021-06-04 | 河北农业大学 | Plant canopy structure character analysis method |
| CN111914901A (en) * | 2020-07-06 | 2020-11-10 | 周爱丽 | Seat arrangement uniformity measuring system and method |
| CN111862315A (en) * | 2020-07-25 | 2020-10-30 | 南开大学 | Human body multi-size measuring method and system based on depth camera |
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